High-contrast Banners Designed to Deter Seabirds from Gillnets Reduce Target Fish Catch
Bibliographic record
Abstract
The incidental catch of non-target species in fishing gear (i.e., bycatch) is a global threat to sustainability and conservation in marine systems. Seabirds experience substantial bycatch mortality, with gillnets having the greatest impacts of any fishing gear. Widespread mitigation to reduce seabird bycatch in gillnet fisheries is tenuous, and information on bycatch in inshore surface-set gillnets remains a major knowledge gap. To help address these issues, we collaborated with commercial fishers to test the efficacy of high-contrast banners designed to alert seabirds. In waters of Newfoundland, Canada, banners were attached to surface-set gillnets for Atlantic Herring Clupea harengus and were compared with simultaneously unmodified control nets within the foraging ranges of major seabird colonies. The banners reduced target catch, creating a non viable option for fishers. Seabird bycatch was low, although it may have been more substantial than indicated by local information sources. Bycatch included fish species of concern (Atlantic Salmon Salmo salar and Porbeagle Shark Lamna nasus). Owing to the episodic nature of seabird and other non-target catch, collaboration with fishers is needed to continue long-term monitoring of inshore gillnet bycatch.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".